Tagging of word and sentence sequences with semantic classes is crucial for natural language processing. Tagging is the identification of meaning and semantics of words and sequences in a “turn”, spoken language that is processed as a discrete instance. Recently, recurrent neural networks (RNNs) with long short-term memory (LSTM) cell structure demonstrated strong results on sequence tagging tasks in natural language processing due to their ability of preserving sequential information from multiple turns of spoken language over time. Generally, however, these RNNs assign tags to sequences considering only their flat structures, i.e., a linear chain of words/phrases. However, natural language exhibits inherent syntactic properties that provide rich, structured tree or tree-like information, which should help computer systems with for better understanding of natural language sentences or phrases, spoken or textual.